An Artificial Intelligence Driven Multi-Feature Extraction Scheme for Big Data Detection

The Internet improves the speed of information dissemination, and the scale of unstructured text data is expanding and increasingly being used for mass communication. Although these large amounts of data meet the infinite demand, it is difficult to find public focus in a timely manner. Therefore, information extraction from big data has become an important research issue, and there are many published studies on big data processing at home and abroad. In this paper, we propose a multi-feature keyword extraction method, and based on this, an artificial intelligence driven big data MFE scheme is designed, then an application example of the general scheme is expanded and detailed. Taking news as the carrier, this scheme is applied to the algorithm design of hot event detection. As a result, a multi-feature fusion clustering algorithm is proposed based on user attention with two main stages. In the first stage, a multi-feature fusion model is developed to evaluate keywords, and this model combines the term frequency and part of speech features. We use it to extract keywords for representing news and events. In the second stage, we perform clustering and detect hot events in accordance with the procedure, and during the composition of news clusters, we analyze several variadic parameters in order to explore the optimal effectiveness. Then, experiments on the news corpus are conducted, and the results show that the approach presented herein performs well.

Abstract licence: CC BY-NC-ND

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